1. Field of the Invention
The present invention relates to an adaptive equalizer used for compensating the transmission delay of a received signal in a receiver of a digital communication system.
2. Description of the Background Art
Recently, digital mobile telecommunications have been intensively developed. One of the problems with the land mobile communications is gross distortion of waveforms of a received signal resulting from frequency selective fading due to fast movement of a mobile station and multiple interference waves accompanying delay. The adaptive equalizer is used for channels whose characteristics change with time to compensate for such distortion.
A conventional equalizer is described, for example, in "Communication Systems Engineering" by J. G. Proakis et al., Prentice Hall, 1994, pp. 577-595, which is incorporated here by reference.
The adaptive equalizers track time variations in the channel response and adapt their coefficients to reduce the distortion. One of such conventional adaptive equalizers is based on an LMS (Least Mean Square) algorithm to estimate the channel characteristics. A received signal y(t) is sampled at a symbol period T to output a sampled sequence {y.sub.n }. The algorithm for optimizing the tap coefficients {Eht(0), Eht(1), . . . , Eh(M)} based on the LMS is expressed as EQU Eht(i).sup.n+1 =Eht(i).sup.n +.beta.*e.sub.n *Ex(n-i), (1)
where Eht(i).sup.n+1 denotes tap coefficients at the (n+1)-th time instant, Eht(i).sup.n denotes tap coefficients at the n-th time instant, .beta. denotes a step-size parameter which is a fixed value, Ex(n) denotes an estimated transmitted symbol sequence, and e.sub.n denotes the difference obtained from the estimated transmitted symbol sequence Ex(n) and the sampled sequence {y.sub.n } where E placed at the initial position of the data names represents that they are an estimate.
Another adaptive equalizer is based on the MLSE (Maximum Likelihood Sequence Estimation). It estimates, when a finite received sampled sequence Y.sub.k ={y.sub.1, y.sub.2, . . . , y.sub.n } is obtained, a transmitted symbol sequence x.sub.k ={x.sub.1, x.sub.2, . . . , x.sub.n } that provides the maximum likelihood of the finite received sampled sequence Y.sub.k ={y.sub.1, y.sub.2, . . . , y.sub.n } under the condition that the impulse response h(t) of the channel is known. This corresponds to obtaining the symbol sequence that maximizes the following expression (2), assuming that the channel noise is the white Gaussian noise. ##EQU1## Expression (2) can be calculated using the Viterbi algorithm.
The conventional adaptive equalizers, however, have the following problems. If there is no delayed wave on the channel, it would be ideal if the values of the delay term taps of the finite tap model would become zero. In practice, however, they have some non-zero amounts because of the estimated error e.sub.n. This decreases the estimation accuracy, which results in the degradation in the demodulation characteristics of a receiver.
The estimation accuracy can be improved by reducing the tracking speed of the delay term taps of the finite tap model of the channel, or by updating the tap coefficients after leaking, that is, after reducing the weighting factors of the past tap coefficients when updating the delay term taps. However, if there is a delayed wave on the channel, the reduction of the tracking speed or the leakage of the tap coefficients presents a problem because the delay term taps themselves must also follow the time variations in the channel. There is another problem in that the demodulation characteristics of the receiver degrade owing to a temporary reduction in the power level of the received signal due to fading or the like.